Filters coefficients in symbol spaced DFE
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From: Vladimir Vassilevsky on 20 Jun 2010 15:56 steveu wrote: >> >>alberto.fuggetta wrote: >> >> >>>Hi, >>> >>>I'm trying to equalize a channel with sever multipath using a DFE > > (12,12) > >>>with LMS adaption algorithm. >>>The relative power of the replicas are quite high w.r.t the main path. > > (max > >>>-4 dB). The equalizer is catastrophic. >>>From the learning curve analysis I can observe that the error is still > > high > >>>after processing the training sequence. >>>Morover, the forward filter coefficients are very small compared to the >>>feedback filter ones (10^-3 vs 0.2). >>>Is there any conclusion I can draw from these info? >>>Thanks >> >>Feedback path adaptation is nasty nonlinear problem. Your filter either >>falls into a local minimum or the adaptation is unstable. > > Or maybe his symbol timing has not been locked down well enough for a one > sample per symbol equalizer to pull in. Trying 2 samples per symbol might > provide insight into the system's behaviour. Feedforward coeffs ~ 0 -> error is not correlated with the signal -> adaptation process is not working right. > Steve >
From: alberto.fuggetta on 21 Jun 2010 15:16 I tried with RLS instead of LMS but I always get bad results. Is there the possibility that the channel is so bad that any algorithm can work well with the DFE? > > >steveu wrote: > >>> >>>alberto.fuggetta wrote: >>> >>> >>>>Hi, >>>> >>>>I'm trying to equalize a channel with sever multipath using a DFE >> >> (12,12) >> >>>>with LMS adaption algorithm. >>>>The relative power of the replicas are quite high w.r.t the main path. >> >> (max >> >>>>-4 dB). The equalizer is catastrophic. >>>>From the learning curve analysis I can observe that the error is still >> >> high >> >>>>after processing the training sequence. >>>>Morover, the forward filter coefficients are very small compared to the >>>>feedback filter ones (10^-3 vs 0.2). >>>>Is there any conclusion I can draw from these info? >>>>Thanks >>> >>>Feedback path adaptation is nasty nonlinear problem. Your filter either >>>falls into a local minimum or the adaptation is unstable. >> >> Or maybe his symbol timing has not been locked down well enough for a one >> sample per symbol equalizer to pull in. Trying 2 samples per symbol might >> provide insight into the system's behaviour. > >Feedforward coeffs ~ 0 -> error is not correlated with the signal -> >adaptation process is not working right. > > > >> Steve >> >
From: cpshah99 on 21 Jun 2010 16:00 >I tried with RLS instead of LMS but I always get bad results. >Is there the possibility that the channel is so bad that any algorithm can >work well with the DFE? > Can you explain your channel model? or how the impulse response looks? Chintan
From: Vladimir Vassilevsky on 21 Jun 2010 16:07 alberto.fuggetta wrote: > I tried with RLS instead of LMS but I always get bad results. Does your equalizer work at all, with a trivial channel, no spread? VLV
From: alberto.fuggetta on 21 Jun 2010 17:35
Yes, it does. If I change the channel with a similar one, having same path delays and lower path gains, the equalizer has good performances. (BER = 10^-4 @ 12 dB) > > >alberto.fuggetta wrote: > >> I tried with RLS instead of LMS but I always get bad results. > >Does your equalizer work at all, with a trivial channel, no spread? > >VLV > |